ICES Journal of Marine Science, 55: 371–391. 1998
Short-term effects of fishing on life history traits of fishes
Marie-Joëlle Rochet
Rochet, M. J. 1998. Short-term effects of fishing on life history traits of fishes. – ICES
Journal of Marine Science, 55: 371–391.
An important aspect of species susceptibility to fishing are the changes in demographic
characteristics of populations that fishing might induce. The purpose of this study is to
show the short-term effects of fishing on growth and reproduction patterns. This
assessment is made through a comparative study of key parameters (mortality, size,
age and size at maturity, fecundity) among stocks subject to various levels of
exploitation. Data have been assembled for 77 separate (primarily commercial) fish
stocks.
Trait variation is partitioned into effects attributable to size, phylogeny, and
population. High adult mortality appears to lead to short-term apparently plastic
changes in age and size at maturity: exploited populations are characterized by earlier
age and increased size at maturity. This compensatory response to exploitation
may conceal longer term selection effects, and may be worth considering in stock
assessments.
1998 International Council for the Exploration of the Sea
Key words: allometry, comparative method, fishing effects, life history, mortality,
phenotypic plasticity, reproduction, teleost fishes.
Received 7 January 1997; accepted 11 November 1997.
Marie-Joëlle Rochet: Laboratoire MAERHA, IFREMER, Rue de l’Ile d’Yeu, B.P.
21105, 44311 Nantes Cedex 03, France: tel: +33 2 40 37 41 21; fax: +33 2 40 37 40 75;
e-mail: [email protected]
Introduction
Demographic traits of fishes, such as growth an reproduction patterns, may change under fishing pressure.
For example, changes in the growth of Pacific Salmon
(Ricker, 1981) and of North Sea sole (de Veen, 1976)
and plaice (Rijnsdorp and van Leeuwen, 1996) have
been documented. Age and size at sexual maturity have
changed for Atlantic cod on the Scotian Shelf (Beacham,
1983), for Atlantic Salmon in North America (Schaffer
and Elson, 1975), for North-east Arctic cod (Jørgensen,
1990) and for many stocks of cod, haddock and other
fish of the North-west Atlantic (Trippel, 1995). Changes
in fecundity have been reported for Atlantic Herring in
the Western Gulf of Maine (Kelly and Stevenson, 1985)
and for North Sea plaice (Horwood et al., 1986;
Rijnsdorp, 1991).
These changes in life history may influence population
dynamics. In some cases, their importance in stock
assessment has been shown by empirical models:
density-dependent growth should be taken into account
for North Sea plaice and haddock, otherwise the potential increases in yield expected from a reduced fishing
intensity or increased mesh size would be overestimated
(Beverton and Holt, 1957). An assessment taking into
1054–3139/98/030371+21 $30.00/0/jm970324
account the compensatory responses of the North-west
Atlantic mackerel stock led to less optimistic yield
projections than classical assessment (Overholtz et al.,
1991).
It has, however, proven difficult to demonstrate the
fishing origin of these changes because of phenotypeenvironment interactions (Pitt, 1975; Hempel, 1978;
Kotilainen and Aro, 1991; Rijnsdorp et al., 1991;
Parmanne, 1992; Rijnsdorp and van Leeuwen, 1992). In
addition, published analyses may result in conflicting
conclusions: for example, under sustained fishing pressure, length at maturity decreased in North Sea cod
from 1920 to 1970 (Hempel, 1978) and in Scotian Shelf
cod between 1959 and 1979 (Beacham, 1983), but
remained stable in Grand Bank American Plaice from
1957 to 1971 (Pitt, 1975), and increased in North Sea
sole between 1957 and 1973 (de Veen, 1976).
Some of these difficulties can be overcome by a
comparative approach that consists of comparing data
from a large number of populations. The effects of
fishing are then statistically separated from residual
environmental fluctuations.
The purpose of this study is to investigate the shortterm effects of fishing on growth and reproduction
patterns. Demographic traits (mortality, size, age and
1998 International Council for the Exploration of the Sea
372
M.-J. Rochet
size at maturity, fecundity) of many fish populations
subject to various levels of exploitation are collated from
the literature. These traits are analysed with methods
developed in the field of evolutionary ecology in order to
separate the effect of fishing from other effects. Relationships between adult mortality, variation in which is
assumed to be mainly due to fishing, and the other traits
are then analysed by multivariate analyses.
Materials
Demographic traits
Traits to be included in such a comparative study may
be numerous (Stearns, 1992; see e.g. Hutchings and
Morris, 1985; Jennings and Beverton, 1991; Beverton,
1992). The main constraint upon trait selection is the
need for reliable estimates of the variables, otherwise the
conclusion of the comparative analysis may be spurious
(Gaillard et al., 1994). We concentrated on female traits
and the following are included:
We define the time-to-5%-survival (T0.05) as the time
elapsed from sexual maturity until 95% of a cohort is
dead. Life span, commonly used as an indicator of
survival, is often estimated by maximum observed
age (Beverton and Holt, 1959; Murphy, 1968; Mann
et al., 1984; Roff, 1984; Hutchings and Morris, 1985;
Jennings and Beverton, 1991; Beverton, 1992). However, such estimates may be biased and highly dependent on the size of the sample used. Krementz et al.
(1989) demonstrated that annual survival rates are
preferable to observed maximum lifespan in comparative life-history studies. However, annual survival rates may vary between age classes for fish.
Moreover, in most fish senescence will not occur and
the mortality patterns in a population will not be
uniform. Therefore time-to-5%-survial is better suited
to fish than life expectation (time-to-50%-survival), as
it integrates mortality rates over most adult life.
Time-to-5%-survival is estimated from an exponential mortality model, on the basis of total mortality
coefficients Z=F+M estimated by VPA or catch
curves. This overcomes the problem of the natural
mortality coefficient M, which is generally poorly
estimated, because errors on M are compensated for
by errors on the estimated fishing mortality F. This
was checked by a sensitivity analysis (Appendix A):
VPA-based estimates of Z are most sensitive to errors
in M in the young partially recruited age-classes
because M/F is high in these age classes relative to
older age classes. These young age classes are not
included in the computation of time-to-5%-survival.
This is valid for exploited stocks with F>M in older
age classes. In unexploited or weakly exploited populations, Z is estimated by a catch-curve. Time-to-5%survival is directly influenced by fishing as it includes
fishing mortality.
Length-at-5%-survival (L0.05) is an adult-size parameter, arbitrarily measured at time-to-5%-survival
because of the indeterminate growth of fishes. The
L` parameter of the von Bertalanffy (1934) growth
model is usually used in comparative studies of
growth or size in fishes (Beverton and Holt, 1959;
Beverton, 1963, 1992; Adams, 1980; Pauly, 1980;
Roff, 1984, 1991; Gunderson and Dygert, 1988;
Jennings and Beverton, 1991). Hutchings and Morris
(1985) use maximum length, which has the same
potential disadvantages as maximum lifespan. Pauly
(1980) defines L` as ‘‘the mean size the fish of a given
stock would reach if they were to grow indefinitely in
the manner described by the [von Bertalanffy] formula’’. We are mostly interested in exploited stocks,
where few, if any, fish have the opportunity to reach
that size. Moreover, L` is generally poorly estimated
in stocks for which data are available only for young
(and small) age-classes.
Age and length at sexual maturity (Am and Lm): we
use length and age at which 50% of the individuals
reach maturity, as published by the authors.
Fecundity (Fb): a mean fecundity per female is difficult to estimate and would include the errors on
many terms (e.g. size structure and size-specific fecundity). It seems therefore preferable to include in the
study the parameters of the fecundity–length relationship: F=aLb. However, the estimate of a is
generally biased because of the log–log procedure
used. Finally we use only the power b of the
relationship, which is the slope of the log–log
fecundity–length regression.
The data are collated from published papers and
Working Groups Reports (listed in Appendix B). One
difficulty is the need for contemporaneous estimates of
all traits for a given stock. The purpose of the study is
indeed to examine the effects of fishing mortality on life
history traits, but we do not know beforehand how
rapidly such effects might appear. I compiled life history
data on 77 populations from 49 species of primarily
commercial teleost fishes; for some of them I have data
for different periods of time. The data are available from
the author.
Phylogenetic information
Most of the phylogenetic information used to remove
the phylogenetic effects (see Methods below) arises from
Eschmeyer (1990) and Lecointre (1994). Additional
information obtained from Lecointre (pers. comm.) permits construction of the topology of a phylogenetic tree
with few unresolved nodes (Fig. 1), based on morphoanatomical characters. Branch lengths however are not
known: DNA sequence data from parts of the genome
can provide information for branch length estimation
(Nei, 1987; Harvey and Pagel, 1991), but they are not
Short-term effects of fishing on life history traits of fishes
373
Engraulis capensis (cap)
Engraulis encrasicholus (enc)
Sprattus sprattus (spr)
Clupea harengus (har)
Sardina pilchardus (pil)
Argentina silus (sil)
Salvelinus alpinus (alp)
Gadus morhua (mor)
Salvelinus malma (mal)
Mallotus villosus (vil)
Melanogrammus aeglefinus (aeg)
Micromesistius poutassou (pou)
Trisopterus esmarki (esm)
Molva dypterygia (dyp)
Merluccius gayi (gay)
Merluccius hubbsi (hub)
Merluccius merluccius (mer)
P
Merluccius productus (pro)
Lophius budegassa (bud)
Atherina boyeri (boy)
Scomberomorus cavalla (cav)
Scomber scombrus (sco)
Trachurus trachurus (tra)
Lethrinus mahsena (mah)
Mullus surmuletus (sur)
Pseudupeneus prayensis (pra)
Lutjanus purpureus (pur)
Lutjanus synagris (syn)
Rhomboplites aurorubens (aur)
Epinephelus cruentatus (cru)
Dicentrarchus labrax (lab)
Pagellus bellottii (bel)
Pagellus erythrinus (ery)
Boops boops (boo)
Pachymetopon blochii (blo)
Pagrus pagrus (pag)
Argyrozona argyrozona (arg)
Pomatoschistus minutus (min)
Pomatoschistus microps (mic)
Gobius niger (nig)
Solea solea (sol)
Pleuronectes platessa (pla)
Platichthys flesus (fle)
Limanda limanda (lim)
Microstomus pacificus (pac)
Hippoglossus stenolepis (ste)
Gymnapistes marmoratus (mar)
Sebastes mentella (men)
Sebastes alutus (alu)
Figure 1. Phylogenetic tree of the sample of teleost fishes analysed. Data from Lecointre (1994, and pers. comm.). The position of
taxa along the vertical axis has no phylogenetic meaning. P: Perciforms. In parentheses: species coding for Figure 4.
374
M.-J. Rochet
Table 1. Relationships between the logarithm of demographic
trait and the logarithm of length-at-5%-survival (L0.05) in 77
populations of fishes: correlation coefficients (r), allometric
slopes (â) standard error, and robust allometric slopes (âr).
Demographic trait
Age at maturity Am
Length at maturity Lm
Time-to-5%-survival T0.05
r
â
âr
0.73
0.97
0.61
0.710.08
0.930.03
0.600.09
0.72
0.89
0.48
available for all populations of our study. Moreover,
branch length estimations depend dramatically on the
part of the genome used in the analysis, and the criteria
to be used in selecting the part of the genome to be
analysed are unclear. Therefore, branch lengths were
arbitrarily set as follows: branch length=5 from populations to species, 1 from species to genera, and 4 for all
other branches (i.e. the distance between two conspecific
populations is 10, between two congeneric populations
of different species is 12, between two populations of
different genera of the same family is 20, etc). Other
combinations of arbitrary distances were used in order
to test the sensitivity of the results to branch lengths.
The sensitivity of the results to incomplete knowledge of
the topology of the phylogeny is also checked by comparing the results of Perciforms considered as monophyletic (Fig. 1) or polyphyletic (Fig. 1 with length of
branch ‘‘P’’ set to zero).
Methods
Allometry
Most life history traits change with body size. Size is a
major constraint upon metabolic rates and energy
assimilation, and thus affects the entire lives of animals,
including growth and reproduction (Reiss, 1989) and
survival (Calder, 1985). The analysis of the correlations
between several of these traits must take into account
the fact that size may act as a confounding variable: two
traits may be correlated because each is correlated with
size. Comparative studies therefore usually remove
size-effects (Harvey and Pagel, 1991).
Size-effects are usually described by the power relationship Y=áLâ, where L is body size, Y is any trait
varying with size, á and â are the parameters of the
equation. The slope â of the transformed equation
ln(Y)=ln(á)+â ln(L) was estimated by least-squares
regression for each trait (except the parameter b of the
fecundity–length relationship, which was already scaled
for length), with length-at-5%-survival as the scaling
variable. A fair agreement (Table 1) was found between
the values of slopes and the expected values of 0.75
generally attributed to time variables (e.g. Platt and
Silvert, 1981; Calder, 1985; Brown et al., 1993), and of 1
for length at maturity. The estimated values were then
used for removing the part of variation of each trait
related to body size: subsequent analyses were performed on size-corrected traits (i.e. on ln(Am)0.71
ln(L0.05); ln (Lm)0.93 ln(L0.05); ln(T0.05)0.60
ln(L0.05)), and results are discussed relative to body size.
Removing phylogenetic correlation
In the framework of evolutionary ecology, many comparative methods have been developed. They allow one
to test hypotheses by comparing the attributes of many
species. Ad hoc methods are needed because species
are not statistically independent, but share a common
history through their phylogeny: they evolved from
common ancestors (e.g. Harvey and Pagel, 1991;
Stearns, 1992). I shift this approach by one step and
compare populations instead of species. Doing so, I
intend to separate phylogenetic and long-term evolved
effects from short-term and local effects, which should
be influenced by the present environment, especially
fishing.
Among the different comparative methods available,
we cannot use those derived from Felsenstein’s method
of pairwise independent comparisons or contrasts
(Felsenstein, 1985; Garland et al., 1992; Purvis and
Rambaut, 1995) because these methods require the
reconstruction of higher node (ancestors) traits, usually
by averaging traits of descendants. In the case of
exploited fish populations, we know that the influence of
fishing on the mortality of populations has increased
with time. Therefore we are not allowed to reconstruct
past traits on the basis of simple assumptions. For the
same reason, we cannot use nested ANOVA or similar
methods, which estimate mean traits of higher taxonomic levels (Stearns, 1983; Harvey and Clutton-Brock,
1985; Bell, 1989). We use the so-called phylogenetic
autocorrelation method, developed by Cheverud et al.
(1985) and Gittleman and Kot (1990); the explanations
below are mainly based on the latter paper. This method
allows one to partition the phenotypic value of a trait
into a phylogenetic component (reflecting common evolution) and a population component (environmentdependent). The method is based on phylogenetic
distance (in a cladogram or phylogenetic tree) and
spatial autocorrelation statistics, which measure effects
that vary with distance.
Moran’s I statistic is an autocorrelation coefficient
that measures the extent to which each observation
resembles that of its neighbours. It is used to answer
the question: Are there phylogenetic effects?
This autocorrelation can be evaluated at different
levels and brought together into so-called ‘‘phylogenetic correlograms’’ to determine the taxonomic
level at which phylogenetic correlation occurs. Here I
computed Moran’s I to measure autocorrelation
Short-term effects of fishing on life history traits of fishes
between populations within species, between species
within families, and between families within orders;
these levels are dictated by the composition of the
data set.
Finally the method allows one to remove phylogenetic correlation by means of an autoregressive
model, which takes the form Y=ñWY+å, where Y is
a vector of observed trait values, ñ is an autocorrelation coefficient, W is a weighting (neighbouring)
matrix, and å are the residuals. ñWY estimates the
phylogenetic part of the trait, and å the population
(environmental) component. The weighting matrix W
is derived from a distance matrix D describing the
relative positions of the populations on a phylogenetic tree by: wij =1/dáij (neighbouring between
populations i and j). á is a flexibility parameter that
accounts for the levels at which autocorrelation
occurs. Large á values describe reduced influence of
distant populations on the estimated trait – consequently higher influence of close neighbours.
A maximum-likelihood procedure is used to estimate
the parameters ñ and á of the autoregressive model. The
ability of the model to remove phylogenetic correlation
is checked by R2, which estimates the proportion of total
variance accounted for by phylogeny, and by a phylogenetic correlogram of the residuals (which should not
be significant).
Multivariate analysis
Whereas the above fits were performed for each trait
separately, we analyse the residuals together by means of
a principal component analysis (PCA; Lebart et al.,
1984). PCA is performed on the correlation matrix of
size-corrected and phylogeny-corrected traits to quantify
the residual covariations of life-history traits: the
relationships between their environmental component,
which variations are assumed to be mainly due to
fishing.
Results
Phylogenetic autoregression
All five of the traits in our study show phylogenetic
autocorrelation at some level (Table 2). Length-at-5%survival is the most autocorrelated trait, and also the
only one with significant autocorrelation between families within orders. On the other hand, length at maturity
is weakly autocorrelated: most of the variance is
explained by size (Table 1) and its residual variance may
be mostly explained by population factors.
We used autoregressive models to partition each
demographic trait into a phylogenetic and a population
component (Table 3). á estimates vary from 1.3 to 4.3,
reflecting the levels at which most autocorrelation
375
Table 2. Phylogenetic correlograms, enumerating normalized
Moran’s I for each trait (to be interpreted as correlation
coefficients). *: 0.05<p<0.01; **: 0.01<p<0.001; ***: p<0.001;
otherwise p>0.05. Am: age at maturity; Fb: parameter b of the
fecundity-length relationship; Lm: length at maturity; L0.05:
length-at-5%-survival; T0.05: time-to-5%-survival.
Taxonomic
rank
Species
Family
Order
Am
Lm
L0.05
Fb
T0.05
0.60***
0.31***
0.08*
0.05
0.11
0.03
0.74***
0.44***
0.15***
0.33**
0.27***
0.03
0.49***
0.22**
0.11
Table 3. Autoregressive model: maximum likelihood estimates
of the model’s parameters á (distance exponent) and ñ (autocorrelation coefficient), and R2-statistic (proportion of total
variance accounted for by phylogeny) for each trait.
á
ñ
R2
Am
Lm
L0.05
Fb
T0.05
3.1
0.965
0.36
1.3
0.827
0.01
4.3
0.977
0.73
2.1
0.980
0.09
2.4
0.912
0.17
occurs. The variance accounted for by phylogeny is
quite variable, ranging from less than 10% in length at
maturity and the parameter b of fecundity to more than
70% for length-at-5%-survival (Fig. 2). In order to check
if the model removes all phylogenetic correlation, we
computed the phylogenetic correlograms for the
population components of each trait (Table 4). All
autocorrelation coefficients become non-significant,
demonstrating the ability of the model to remove
phylogenetic correlation.
Multivariate analysis
Correlation matrices of size-corrected demographic
traits and of their phylogenetic and population
parts show different patterns of correlation (Table 5).
Spearman’s rank correlations were estimated because
the distribution of the residuals is unknown: most of the
observed correlations are robust, especially the ones
discussed below (Table 6). Interestingly, more correlation coefficients are significant in the phylogenetic
components matrix than in the size-corrected traits
matrix (Table 5: five significant correlation coefficients in
phylogenetic components, two in unpartitioned traits).
Moreover, two of these correlation coefficients have
opposite signs in the phylogenetic and population components (Table 6: r(Lm, T0.05)>0 for the phylogenetic
component, <0 for the population component; r(Fb,
L0.05)<0 for the phylogenetic component, >0 for the
population component): in the phylogenetic component
376
M.-J. Rochet
Length at maturity
Length-at-5%-survival
0.2
4
0.0
2
–0.2
–0.4
0
–0.6
T
T
0
20
40
0
80
60
20
40
60
80
20
40
60
80
0.2
4
0.0
2
–0.2
–0.4
0
P
–0.6
P
20
0
40
60
0
80
0.2
4
0.0
2
–0.2
–0.4
0
R
–0.6
C
0
R
Ar S
G
A
20
P
40
Pl
Sc
C
80
60
0
Ar S
20
G
A
40
P
Pl
60
Sc
80
Figure 2. The trait vector (T) and its phylogenetic (P) and residual (R) components. (Left) Plot of size-corrected length at maturity
for 77 populations of teleost fishes from the orders Clupeiformes, Argentiniformes, Salmoniformes, Gadiformes, Atheriniformes,
Perciformes, Pleuronectiformes, and Scorpaeniformes, and its partition into phylogenetic and population components. (Right)
Same decomposition for length-at-5%-survival. The abscissa is ordered from the most ancestral to the most derived taxon used in
the analysis.
of the traits, short life is associated with small size and
early age at maturity.
Principal component analysis of the population parts
of these traits exhibits the positive correlation of timeto-5%-survival with age at maturity, and its negative
correlation with length at maturity (1st axis); and a
positive link between length at maturity and the parameter b of fecundity (2nd axis; Fig. 3). Time-to-5%survival is the variable with the highest contribution to
Table 4. Phylogenetic correlograms, enumerating normalized
Moran’s I for the population component of each trait. All
autocorrelation coefficients are unsignificant at the 0.05 level.
Taxonomic rank
Am
Lm
L0.05
Fb
T0.05
Species
Family
Order
0.13
0.03
0.03
0.00
0.03
0.01
0.08
0.06
0.05
0.08
0.12
0.00
0.15
0.01
0.06
the structure of the analysis (with the highest loading for
the first component: 12.7% of total variability). On the
other hand, length at maturity and its positive link with
Fb appear on the orthogonal second component,
although their correlation coefficient is not significant
because of non-linearity. This may be interpreted as a
phenotypic trade-off between length-at-maturity and the
slope of the fecundity–length relationship: individual
fecundity in populations with a large size at maturity
would potentially increase steeply with length, and
conversely; this trade-off seems independent of
time-to-5%-survival, and also of fishing.
On the plot of the populations’ first two components
(Fig. 4a), populations of all taxa appear mingled and not
sorted by taxa: as the method removes the phylogenetic
part of the variation, the residuals are free of this
constraint. In order to display the effect of fishing on the
patterns in life history, I plotted the first two components together with an expanding symbol proportional
to the ratio of fishing mortality to natural mortality
Short-term effects of fishing on life history traits of fishes
Table 5. Correlation matrices of (a) size-corrected demographic
traits; (b) their phylogenetic components; (c) their population
components. Only significant correlation coefficients at the 0.05
level are shown.
(a) Demographic traits
Am
Lm
L0.05
377
1.0
Fb
0.5
1
0.51
0.24
(b) Phylogenetic components
Am
Lm
1
1
Axis 2
T.05
Lm
L0.05
Fb
T0.05
0.0
Am
L0.05
L.05
Fb
–0.5
Lm
L0.05
Fb
T0.05
0.21
0.67
(c) Population components
Am
Lm
L0.05
Fb
T0.05
1
Lm
1
0.36
0.31
Fb
1
0.32
0.34
1
0.25
L0.05
Fb
1
0.24
1
Table 6. Spearman’s rank correlation matrices of (a) sizecorrected demographic traits; (b) their phylogenetic components; (c) their population components. Only significant
correlation coefficients at the 0.05 level are shown.
(a) Demographic traits
Am
Lm
Lm
L0.05
Fb
T0.05
0.54
(b) Phylogenetic components
Am
Lm
Lm
L0.05
Fb
T0.05
0.22
1
0.66
(c) Population components
Am
Lm
L0.05
Fb
T0.05
1
1
Fb
1
L0.05
Fb
0.28
1
0.42
0.30
1
Lm
L0.05
Fb
1
0.23
1
1
0.38
L0.05
0.29
Lm
–1.0
–1.0
–0.5
0.0
Axis 1
0.5
1.0
Figure 3. Scaled principal component analysis of the population parts of five demographic traits: plot of the original
variables on the first two principal axes. The first two axes
account for 56% of total variance: Am: age at maturity; Fb:
parameter b of the fecundity–length relationship; Lm: length at
maturity; L0.05: length-at-5%-survival; T0.05: time-to-5%survival.
time-to-5%-survival is mainly due to fishing. The first
component shows a gradient from unexploited stocks
with delayed maturity at a small size (e.g. unexploited
char of Baffin Island alpO7 and South African Sparidae
argA6 and bloA6, left) to short-lived stocks with precocious maturity at a large size (e.g. Mediterranean goby
minM8, West African red mullet praA7, Cuban snapper
synO7, right). The case of North Sea plaice is of special
interest: in the forties, after fishing was interrupted
during Second World War, the population has the
mortality and maturity traits of an unexploited population (plaN4). In the 1970s, the population is among
other exploited populations (plaN7).
Sensitivity analyses
(Fig. 4b). As fishing and natural mortality are difficult to
estimate, the ratio was converted into four categories.
Although most of these figures are not perfectly reliable,
their overall distribution is consistent with the assumption that variation in the population component of
Autoregressive models were fit to the data with different
branch lengths: (i) as described previously; (ii) all
branches=1; (iii) branch length=1 from populations to
species and from species to genera, and 4 for all other
branches. Estimates of á consistently increase as the
species-population distance increases, whereas ñ estimates and the proportion of total variance accounted
for by phylogeny remain constant. The ability of the
model to remove phylogenetic correlation is slightly
better with set (i): all autocorrelation coefficients become
non-significant, whereas L0.05 autocorrelation remains
significant at the family level with sets (ii) and (iii). As
the results of the subsequent multivariate analysis
378
M.-J. Rochet
(a)
2
Axis 2
1
0
–1
–2
(b)
2
Axis 2
1
0
–1
–2
–2
0
Axis 1
Figure 4.
2
Short-term effects of fishing on life history traits of fishes
did not differ significantly, set (i) was used in further
computations.
A bias may arise in the results because cod populations used in the study are all under high fishing
pressure. Because of the difficulty of finding data on
weakly exploited stocks of cod, the influence of the
species on final results was checked by comparing the
results with and without cod. Autocorrelation and
autoregression coefficients are similar; some correlation
coefficients of the model and residual matrices become
unsignificant, but the ones discussed here are robust.
The same kind of result is obtained when removing all
Gadidae with a short time-to-5%-survival.
Comparing the results of considering Perciforms as
polyphyletic or monophyletic shows that the results of
the analysis are not sensitive to this uncertainty in the
topology of the phylogenetic tree.
Discussion
Autoregressive method
The autoregression method proves able to remove most
of phylogenetic autocorrelation. Whereas the partition
into phylogenetic and population components was performed on each trait independently, there remains significant correlations in the population components
matrix (Table 5). This argues for the ability of the model
to separate components which reflect actual parts of the
variability. Moreover, more correlation coefficients are
significant in the phylogenetic and population components than in the original size-corrected traits: the
method seems able to make visible patterns that were
concealed in the original data.
I used Gittleman and Kot’s method (1990) as suggested in their conclusion: I correlated the residual
components of several traits together, and tested these
correlations independently of confounding phylogenetic
correlations. As these authors have pointed out, their
method is appropriate only if there is sufficient genetic
variation in the traits. Although life history traits have
lower heritabilities than morphological or behavioural
traits, their heritabilities are significantly greater than
zero (Stearns, 1992). Moreover, there is plenty of genetic
variation for life-history traits in natural populations.
Therefore it seemed appropriate to apply the autoregression method at the population level, in order to
379
take into account intraspecific variation in interspecific
comparisons.
Phylogenetic information and sampling
Phylogenetic information to be used in comparative
studies should be as complete and accurate as possible
(Harvey and Pagel, 1991). The phylogenetic autocorrelation method may result in type I errors when the
topology of the phylogeny is known very incompletely
(Purvis et al., 1994). In the case of teleost fishes, the
topology of the tree is well known, except the Perciform
branch (Lecointre, 1994), but the results of our study are
not sensitive to this uncertainty.
A good knowledge of branch lengths is also required
but seldom available (Harvey and Pagel, 1991). Branch
lengths elaborated on a common basis are unavailable
for the sample of populations used here. But the phylogenetic autoregression method is not the most sensitive
to incorrect branch lengths (Purvis et al., 1994) because
the parameter á introduces flexibility with regard to
distances. The study is representative only of the mostly
commercial stocks used in the sample and not of all
teleost fishes. This is because sampling was constrained
by the availability of data, and it was not possible to
obtain a good sampling across phylogeny: many taxa are
lacking and represented taxa are not given an equal
weight.
Another sampling difficulty arises from the fact that
different populations of one species are often exploited
with a similar intensity; an extreme case is cod, which
has only highly exploited populations. Ideally the study
should involve together exploited and unexploited populations of each species, because the autocorrelation
method will ascribe to phylogeny the common features
of conspecifics. Nevertheless, the low á value estimated
for time-to-5%-survival (2.4) indicates that distant populations still have some weight in predicting this trait:
conspecifics are not the only populations taken into
account in estimating time-to-5%-survival of a given
population.
The population component
With regard to the population component, long-lived
populations appear to have a small size a maturity at a
late age, whereas high adult mortality is associated with
Figure 4. (a) Scaled principal component analysis of the population parts of five demographic traits: the first two principal
components of populations. Coding of the populations: first three letters=species name (see Fig. 1); capital letter=location (A:
South Atlantic. B: Baltic Sea. C: Channel and Celtic Sea. E: North East Atlantic. G: Bay of Biscay. I: Indian Ocean. M:
Mediterranean. N: North Sea. O: West Atlantic. P: Pacific Ocean); number=decade of the XXth century (e.g. 6 means during the
sixties). (b) Same figure with an expanding symbol proportional to the ratio of fishing mortality to natural mortality for each
population (dots: no fishing mortality. Small circles: 0<F/Mc1. Intermediate circles: 1<F/Mc2. Large circles: F/M>2).
380
M.-J. Rochet
Table 7. Changes in size at maturity in various exploited populations (length at which 50% of the individuals reach maturity).
Reference
Species
Location
Period
Sex
Pitt (1975)
American plaice
ICNAF Div. 3L
1961–1972
ICNAF Div. 3N
1957–1971
F
M
F
M
F
F
F
M
F
de Veen (1976)
Bowering (1989)
Rijnsdorp (1989)
Sole
Witch flounder
North Sea plaice
North Sea
Newfoundland
Dogger
1957–1973
1950–1986
1904–1986
Beacham (1983)
Cod
Southern Bight
Scotian Shelf
1959–1979
Hempel (1978)
Cod
North Sea
1920–1970
Rowell (1993)
Cod
North Sea
1893–1974
Clark and Tracey (1994)
Orange roughy
New Zealand
1984–1990
early maturity at a large size. The shift of North Sea
plaice in this trade-off when exploited or not, is an
argument for an environmental component of the variability of these traits. Time-to-5%-survival is the most
contributing variable to the structure of this environmental component; the exploitation level is associated
with this structure (Fig. 4b). Both arguments indicate
that fishing would be an important aspect of this
environment. These short-term environmental effects are
of larger magnitude than phylogenetic components, as
evidenced by the proportion of total variance accounted
for by phylogeny for age and size at maturity and
time-to-5%-survival (Table 3). I interpret these shortterm environmental effects as the expression of the
phenotypic plasticity of growth and maturation in fishes.
Phenotypic plasticity was defined by Schmaulhausen
(1949) as the ability of a genotype to express various
phenotypes across an environmental gradient. Although
phenotypic plasticity of growth has been commonly
observed, especially density-dependent growth (e.g.
Beverton and Holt, 1957; Hubold, 1978; Ross and
Almeida, 1986; Overholtz, 1989), in my results it appears
mainly in the variations of size and age at maturity,
because I worked relative to size by removing size
effects.
These results show a good agreement with available
experimental evidence. Alm (1959) reports that in some
experiments with brook trout, size at maturity increases
and age at maturity decreases as growth rate increases.
Reznick (1993) showed by a series of experiments on
guppies that increased resource availability (which may
be in natura a consequence of fishing, which reduces
population densities) causes an increase in the size at
maturity and a decrease in the age at maturity. Another
F
M
F
M
F
M
Size at maturity (cm)
at beginning and
end of period
Change in
size at age
41.5–41.1
25.5–26.0
43.7–42.8
25.5–25.3
27.5–30.0
46.0–43.0
41.0–35.0
35.0–24.0
34.0–30.0
64.0–43.0
61.0–44.0
75.0–60.0
65.0–50.0
75.0–61.0
73.0–54.0
27.1–22.3
25.7–24.5
Increased
Increased
Increased
Increased
Increased
Increased
Increased
Increased
Increased
No change
No change
No change
No change
No change
No change
Not available
Not available
experiment by Belk (1995) on bluegill sunfish shows that
the variance in growth patterns and age and size at
maturity observed between populations apparently
resulted mostly from environmental differences, not
from genetic variation; and that observed patterns of
variation between populations are best explained by
effects of differences in predator populations.
Many observations of maturity changes under fishing
pressure in natural populations have been reported. Age
at maturity has been observed to decrease under exploitation in Grand Bank American plaice (Pitt, 1975), in
Scotian Shelf cod (Beacham, 1983), in halibut caught
in Northern Norway (Haug and Tjemsland, 1986), in
North-east Arctic cod (Jørgensen, 1990) and in North
Sea plaice (Rijnsdorp, 1989). Trippel (1995) compiled
data for 8 cod stocks and 4 haddock stocks of the
North-west Atlantic, and 11 other fish populations: in
all but one age at maturity of both males and females
declined under exploitation, as much as 1 to 3 years or
more. Age at maturity was much higher in the unexploited cod of Ogac Lake than in any of the neighbouring exploited stocks (Patriquin, 1967). Also age at
maturity decreased as stock biomass decreased (indirect
effect of fishing) in North Sea herring (Hubold, 1978),
North Sea cod (Hempel, 1978) and Grand Bank cod (Xu
et al., 1993).
The negative association between longevity and size at
maturity is more unexpected. This finding is in accordance with experimental evidence (Alm, 1959; Reznick,
1993) and with theoretical predictions (Stearns and
Koella, 1986; Hutchings, 1993). However, most of the
published studies based on field data conclude that
length at maturity decreases or remains stable under
fishing pressure (Table 7). Most of these changes are of
Short-term effects of fishing on life history traits of fishes
low magnitude. Still we have to explain this discrepancy.
Comparing results on size at maturity is difficult for two
reasons. (i) I corrected size at maturity by adult size,
whereas other studies did not. Exploitation removes
large individuals and hence reduces the mean size of fish
in the population. As L0.05 decreases, ln(Lm)0.93
ln(L0.05) may increase if the variation in Lm is low.
However, correction by size is necessary because otherwise size would act as a confounding variable, and in
order to take into account, when comparing size at
maturity, the concomitant changes in length at age and
in the mean size of the population. Moreover, from a
theoretical point of view, the dimensionless number
Lm/L` was considered useful in looking for life history
generalization (Charnov and Berrigan, 1991). Beverton
(1963) and Beverton (1992) used this number in comparative studies and concluded that it tends to be
constant within taxa. I also find that adult size explains
the most part of length at maturity (94%: Table 1), but I
seek to interpret the remaining variation. (ii) The second
difficulty arises from the fact that most of published
studies of Table 7 consider relatively long series (20
years or more): in these observations long-term effects of
fishing may be entangled with short-term plasticity we
are dealing with. For age at maturity long-term and
short-term effects act in the same direction, but this is
not the case for length at maturity (Table 6; Reznick,
1993). This is an advantage of large comparative studies
such as ours, which allow one to disentangle long-term
and short-term effects.
Another interesting observation, although not directly
related to fishing effect, is the positive correlation
between the slope of the fecundity–length relationship
and L0.05: populations with a larger size have a higher
fecundity–length slope than populations with a smaller
size (Tables 5 and 6). This pattern is not apparent on
Figure 3 but on the fourth axis of the PCA (not shown).
The slope of the fecundity–length relationship is also
positively associated with size at maturity (Fig. 3). These
patterns may be interpreted in terms of phenotypic
trade-offs. In fish the widespread trade-off between
current and future reproduction (Stearns, 1992) appears
in the strong negative correlation between the parameters a and b of the fecundity–length relationship
F=aLb (from my data set: r(a,b)= 0.85, and 0.88
when corrected for size effects): the lower fecundity is at
maturity, the steeper it can increase later. After maturity
the surplus power, i.e. the fraction of ingested energy
remaining after an individual has paid metabolic costs, is
to be shared between growth and reproduction (Ware,
1984). We may hypothesize that if fecundity is high at
maturity, a lower part of the surplus power remains for
growth, and that consequently fish reach a smaller size.
This phenotypic trade-off would be consistent with the
one observed by Jennings and Beverton (1991) in a
comparative study of life history traits of nine stocks of
381
Atlantic herring: a low annual reproductive output
is associated with late maturity and slow postmaturational growth towards a large maximum size
(and vice versa).
Theoretical aspects and mechanisms of the
plasticity of size and age at maturity
Several theoretical models have been developed to predict the reaction-norm of size and age at maturity in
fishes, with different assumptions on the constraints that
will act on the reaction-norm (Stearns and Crandall,
1984; Stearns and Koella, 1986; Perrin and Rubin, 1990;
Roff, 1992). These models may not be suitable for
exploited populations, because they assume a stable age
distribution (Stearns and Crandall, 1984). Some of these
models predict dome-shaped norms of reaction for sizeto-age at maturity in fishes, which fit Alm’s (1959)
observations. Stearns and Crandall (1984) show that
other shapes of these norms of reaction are plausible
under different assumptions. The difficulty in interpreting these conclusions is that a high adult mortality is
usually not taken into account as a constraint that
may act directly or indirectly on this reaction-norm.
Hutchings (1993) constructed reaction-norms by determining how optimal age and size at first reproduction
varied with growth rate in two unexploited brook trout
populations. Harvesting might result in decreased juvenile density and in increased growth rate; optimal size at
maturity increases with growth rate in one population
and varies little in the other population, whereas optimal
age at maturity declines as growth rate increases in both
cases. Moreover, size selective mortality might change
the slope of the reaction norm.
How adult mortality due to fishing may act on growth
and maturation processes is not clear. The main hypothesis is that fishing decreases the population’s density,
that growth would be density-dependent, and that the
norm of reaction for size-to-age at maturity would be
constrained by growth. Evidence of density-dependent
growth is abundant in various populations of distantly
related taxa (e.g. Beverton and Holt, 1957; Hubold,
1978; Ross and Almeida, 1986; Overholtz, 1989).
Growth-dependent plasticity of size and age at maturity
was also demonstrated by experimental evidence (Alm,
1959; Reznick, 1993; Belk, 1995) and by field data on
lake whitefish (Jensen, 1981). However, other mechanisms may act, for example Rijnsdorp et al. (1991), in an
extensive study of the variations of growth, maturation
and fecundity in several North Sea stocks, suggested
that the observed compensation in total egg production
may be due to effects of reproductive variability related
to the age composition of the spawning population,
rather than to density-dependent effects.
Other possible phenotypic effects of high adult mortality may be mediated by maternal effects, defined by
382
M.-J. Rochet
Bernardo (1996) as direct effects of a parent’s phenotype
on the phenotype of its offspring. Maternal effects
appear as a kind of cross-generation phenotypic plasticity, and may affect many demographic traits, including
offspring growth and recruitment. Maternal effects are
largely documented in mammals, birds, salamanders,
lizards, and plants (Bernardo, 1996). Reznick et al.
(1996) demonstrated experimentally that food availability to mother Poeciliid fishes affects the size of young.
Chambers and Leggett (1996) use comparative methods
to show that egg size is likely to be significantly modulated by the maternal environment during oogenesis in
marine teleost populations; differences in egg size may be
propagated through growth into variation in sizes of
larvae, juveniles and even adult fishes. Investigating the
timing of short-term fishing effects would allow to assess
the importance of maternal effects in the observed
phenotypic plasticity.
Phylogeny of phenotypic plasticity
This paper concentrates on the population component
of teleost life history traits, and investigates fishing
effects on life history tactics. The complementary component of these traits, namely the phylogenetic component, is worth investigating in detail. This would give
more basic insights into teleost demographic strategies,
which are the range of flexible tactics a species may
deploy, depending on the environment (Wootton, 1984).
This would allow a re-examination of the conclusions of
pioneering works about this subject by Beverton and
Holt (1959), Beverton (1963), Adams (1980) and Roff
(1984), who did not address the problem of separating
long-term evolved effects from environmental effects.
This study is based on the hypothesis that the phenotypic part of the answer to fishing would be the same in
all taxa of teleost fishes: this phenotypic answer is indeed
the residual variation of traits, once the resemblance of
phylogenetically close populations has been removed.
Hence this work will only reveal the general features of
this plasticity throughout our sample of teleost fishes.
But it seems plausible that different taxa would have
evolved different plasticity patterns in response to a
given stress. Table 7 suggests that average size at age
increases under fishing pressure in Pleuronectiformes,
but remains unchanged in cod. Also Rijnsdorp et al.
(1991) show that compensation for fishing by enhanced
reproductive output appears in three North Sea stocks
by three different mechanisms (earlier maturation in
plaice, enhanced growth in sole and increased fecundity
in cod). Their study has the disadvantage of long-term
and short-term effects being entangled (30 years series
analysed), and this is a common feature to most studies
of fishing effects. There is a need for investigating the
differences in short-term responses to fishing in different
teleost taxa.
Another criticism that may be addressed to this study
is that life-history traits are co-evolved: there are linkages between traits, that constrain the simultaneous
evolution of two or more traits (Stearns, 1992). Here we
investigate phylogenetic autocorrelation trait by trait,
and hence leave co-evolution out of account. This is a
general gap in comparative methods, most of which
consider the variations of no more than two traits
(Harvey and Pagel, 1991); for example, Gittleman (1986)
and Read and Harvey (1989) successively investigated
the covariations of many pairs of life history traits. A
multivariate method that would test for differences in
the relationships between several traits, independently of
phylogenetic correlation, would be an important
advance in this field.
The effects of fishing and fisheries management
Fishing has three types of effects on fish population
dynamics: direct effects on population density and on
the mean size of individuals; short-term environmental
effects on growth and reproduction, mediated by phenotypic plasticity and density-dependent mechanisms; and
long-term effects due to the selective pressure imposed
by harvesting. Whereas the problems raised by the first
type of effects are commonly addressed by fisheries
assessment models and management policies, the other
two types of effects are seldom considered.
This is partly because these effects are difficult to
separate, and may be contradictory. In the phylogenetic
component of our traits, short life is associated with a
small size and an early age at maturity; this result is in
agreement with Reznick (1993) who showed experimentally that, in the longer term, increased adult mortality
selects for earlier maturity at a smaller size. Hence we
support the conclusion of this author, that compensatory responses of populations to fishing may conceal
longer-term selection effects.
Moreover, Nelson (1993) emphasizes that the evolutionary response to selection and its detection depend
upon the joint reaction norms of growth and reproduction to changes in density and environmental factors;
only when these joint reaction norms have been identified can their evolutionary change be detected. This
work is a first step towards this aim: in a rather large
sample of teleosts, we document evidence that fishing
imposes a phenotypic response in growth and reproduction. It would be worth investigating the shape and
genetic variation in the reaction norm. Its influence on
exploited population dynamics should be checked: the
empirical works of Beverton and Holt (1957) and Overholtz et al. (1991) may be given a more basic support in
order to develop general models, suitable to various
populations.
On the other hand, fishing applies a selective pressure
that may shift the long-term selective advantage from a
Short-term effects of fishing on life history traits of fishes
reproductive pattern to another (Ware, 1984). Further
work should use our first results on short-term effects to
investigate these changes.
Summary
This work demonstrates a general trend of the shortterm effects of fishing on demographic traits in a large
sample of teleost fishes: populations develop compensatory responses to fishing by decreasing their age at
maturity and increasing their size at maturity. These
effects are of large magnitude and may conceal
longer-term selection effects.
These effects should be better analysed and quantified
and, if necessary, taken into account in stock assessment
and management.
A first consequence of this result is the recommendation that maturity ogives used in stock assessment and
projections should be regularly updated.
Acknowledgements
I thank Dominique Pontier for much stimulating discussion during the course of this work and Jean Boucher
and an anonymous referee for valuable comments on
previous versions of this paper. I thank Guillaume
Lecointre (Laboratoire d’Ichtyologie et Service
Commun de Systématique Moléculaire, Muséum
National d’Histoire Naturelle, 43 rue Cuvier, 75231
Paris Cedex 05, France) who provided the phylogenetic
information. I thank Annick Radenac for help in
collating the data, and S. Flatman, L. Motos, N. Perez,
J. C. Poulard, and J. A. Tomasini for making their data
available to me. Christine Maisonneuve drew the phylogenetic tree. This study was partly supported by the
Programme National Dynamique de la Biodiversité et
Environnement.
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Appendix A: Sensitivity analysis of total
mortality to errors in natural mortality
in VPA
The linear sensitivity coefficient of total mortality at age
a Za to natural mortality at age i Mi, Fa being fishing
mortality at age a, is defined as:
385
Which means that, the higher the fishing mortality, the
lower the ratio M/Z and hence the sensitivity.
Which means that total mortality at age a is most
sensitive to natural mortality at age a, relative to natural
mortality at other ages.
An example of S(Za/Ma) for haddock in division VIa
(West of Scotland), 1983–1992 (data from ICES, 1993) is
provided in Table 8.
Appendix B: References of the data
Clupeiformes
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Table 8. Sensitivity of total mortality at age a to natural mortality at age a for West Scotland haddock.
Age
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
Mean
0
1
2
3
4
5
6
7
8
0.973
0.251
0.201
0.288
0.226
0.242
0.197
0.161
0.225
0.982
0.31
0.218
0.123
0.125
0.165
0.153
0.123
0.186
0.986
0.251
0.219
0.192
0.136
0.172
0.124
0.156
0.22
0.944
0.473
0.271
0.177
0.234
0.274
0.322
0.224
0.286
0.721
0.227
0.153
0.137
0.13
0.118
0.073
0.089
0.15
0.343
0.17
0.184
0.134
0.136
0.114
0.22
0.14
0.204
0.829
0.274
0.181
0.159
0.124
0.109
0.068
0.103
0.165
0.973
0.186
0.082
0.125
0.123
0.143
0.252
0.151
0.214
0.726
0.154
0.118
0.11
0.071
0.075
0.092
0.087
0.148
0.725
0.305
0.221
0.15
0.143
0.105
0.107
0.147
0.211
0.82025
0.26006
0.1847
0.13952
0.14485
0.15174
0.16084
0.13807
0.2009
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